See your filing the way AI readers will, before you file it
The next reader of your disclosure is not a human analyst first: it is a fleet of AI systems and quant engines that parse, rank, and score your text. LyraMind runs your draft through that lens and shows exactly where machine readers are likely to misparse it.
The problem: your filing is read by machines before people
Quant engines, thematic aggregators, and financial agents now parse a 10-K, 10-Q, or 8-K and score it in seconds. Hedged, unquantified, or double-negative language that a human would read past can be misparsed by a machine in ways that shape how your name is positioned, sometimes before anyone in IR intervenes.
The question is no longer "does search rank us" but "do the AI systems that increasingly mediate research understand us correctly." Today most CFOs and IR offices have no way to check that before the document is public.
What LyraMind gives you: a pre-file AI Readiness Report
Paste in your draft disclosure text and LyraMind returns a legibility verdict (AI_LEGIBLE, REVIEW_LANGUAGE, or HIGH_MISREAD_RISK) with the specific spans that drive misread risk and a plain remediation for each.
The board-ready AI Readiness Report composes three engines: the legibility scan, the Institutional AI Simulator (a panel of synthetic institutional archetypes reading your draft), and the Financial Intelligence Graph for peer cohort context. You get sub-scores across institutional, retail, developer, and regulatory readiness, plus explainability, evidence coverage, contradiction clarity, and freshness, and the predicted questions the filing will trigger.
Honest scope: this is an editorial and machine-legibility analysis of your public text. It is not investment advice, a price prediction, or a claim to optimize rankings inside any proprietary model. The public demo runs on sample data.
How you use it
Run a draft through the disclosure scan during review cycles, then generate the full AI Readiness Report for the board pack. Benchmark your score against your peer cohort and track it over time so each filing cycle sharpens.
Every report records to the Trust Ledger, so the analysis is auditable, and feeds the question flywheel that surfaces what readers are actually confused about.
- Pre-file legibility verdict (AI_LEGIBLE / REVIEW_LANGUAGE / HIGH_MISREAD_RISK) with flagged spans and remediations
- Board-ready AI Readiness Report with institutional, retail, developer, and regulatory sub-scores
- Institutional AI Simulator: synthetic analyst archetypes read your draft and surface where they diverge
- Predicted questions your filing will trigger, before it is public
- Peer-cohort benchmark and readiness-over-time monitoring
- Every report recorded to an auditable Trust Ledger
Questions
Does this optimize my filing to rank better inside a specific AI model?
No. LyraMind analyzes the clarity, consistency, completeness, and machine-readability of your public text through transparent, auditable analysis. It does not claim to game rankings inside any proprietary model, and it never predicts how a named model will respond.
Is my draft disclosure sent to third-party models?
The core scan is deterministic and offline-safe: it runs without calling any external LLM. An optional multi-model panel can be enabled behind the same contract when you configure it, and that mode is clearly labeled.
Can I see how my readiness compares to peers?
Yes. The benchmark endpoint compares your AI Readiness score against a peer cohort drawn from the Financial Intelligence Graph, and the history endpoint tracks your score over time. Both are machine-legibility comparisons of public disclosure, not advice.